# Instagram Active Hours & Best Time to Post Analyzer (`seemuapps/instagram-active-hours-analyzer`) Actor Find the best time to post on Instagram for any public account — get a day-by-hour posting heatmap, peak engagement slots, and the account's likely time zone. - **URL**: https://apify.com/seemuapps/instagram-active-hours-analyzer.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** SEO tools, Lead generation, Social media - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $100.00 / 1,000 active-hours reports This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Instagram Active Hours & Best Time to Post Analyzer Find the **best time to post on Instagram** for any public account. Enter a username and get a day-by-hour heatmap of when the account posts, which time slots earn the most engagement, quiet (likely-asleep) hours, and an estimate of the account's time zone — no login or cookies required. ### What you get - **Best times to post** — the day+hour slots where the account's posts earn the highest average engagement (likes + comments, plus plays for Reels) - **Day-by-hour heatmap** — a 7×24 grid showing how many posts and Reels land in each hour of each weekday, in both the account's local time and UTC - **Engagement heatmap** — average engagement per post for every day+hour slot - **Most active day and hour** — the single peak weekday and hour - **Quiet hours** — the hours with little or no activity (the account's likely offline/sleep window) - **Estimated time zone** — auto-detected from the quietest posting window, with a confidence rating (or supply your own) - **Weekday vs. weekend split** — what share of posting falls on weekdays vs. weekends - Export to JSON, CSV, or Google Sheets directly from the Apify console ### Use cases - **Best time to post** — schedule your own content for the slots where a top account in your niche gets the most engagement - **Content calendar planning** — build a posting schedule backed by real engagement data, not generic "post at 9am" advice - **Audience and competitor research** — understand a creator's or brand's posting rhythm and when their audience responds - **Influencer vetting** — spot accounts that post on a flat, automated schedule vs. organic human activity - **Time-zone inference** — estimate where in the world an account is based ### How it works The analyzer samples the account's most recent posts and Reels and buckets every timestamp by weekday and hour. Posting time shows when the account is active; per-slot average engagement shows when its audience responds best. The time zone is inferred from the **circadian dip** — the multi-hour window when the account posts least (its local night) — and you can always override it. > Note: this reflects *posting* activity, not real-time presence. Scheduled/automated posts can flatten the pattern — which itself shows up as a "low confidence" time-zone estimate. ### How to use 1. Enter the Instagram **username** (with or without @; profile URLs work too) 2. Set **Posts to Analyze** (default 500; more samples = a sharper pattern) 3. Optionally set a **Time Zone Offset** (e.g. `-5`, `1`, `5.5`) to skip auto-detection 4. Run the actor — the report appears in the **Dataset** tab ### Output format A single record per run: ```json { "username": "natgeo", "platform": "instagram", "postsAnalyzed": 500, "timeRange": { "earliest": "2025-08-01T...", "latest": "2026-06-09T...", "spanDays": 312 }, "timezone": { "mode": "inferred", "offsetHours": -5, "label": "UTC-5", "confidence": "high", "note": "..." }, "mostActiveDay": "Tuesday", "mostActiveHour": "13:00", "quietHours": ["02:00", "03:00", "04:00", "05:00"], "weekdayShare": 0.74, "weekendShare": 0.26, "engagementMetric": "likes + comments (+ Reel plays where exposed)", "avgEngagementPerPost": 48211, "bestTimesToPost": [{ "day": "Friday", "hour": "16:00", "posts": 9, "share": 0.018, "avgEngagement": 112480 }], "topSlotsByPosts": [{ "day": "Tuesday", "hour": "13:00", "posts": 14, "share": 0.028, "avgEngagement": 51730 }], "hourHistogram": [/* 24 counts, local */], "dayHistogram": [/* 7 counts, Monday-first */], "avgEngagementByHour": [/* 24 averages, local */], "avgEngagementByDay": [/* 7 averages, Monday-first */], "dayNames": ["Monday", "...", "Sunday"], "heatmap": { "Monday": [/* 24 */], "...": [] }, "heatmapUtc": { "Monday": [/* 24 */], "...": [] }, "engagementHeatmap": { "Monday": [/* 24 */], "...": [] } } ```` Build a visual heatmap straight from `heatmap` or `engagementHeatmap`, or chart `hourHistogram` / `avgEngagementByHour` for a quick view. # Actor input Schema ## `username` (type: `string`): Instagram username, with or without leading @. Profile URLs (https://www.instagram.com/username) are also accepted. ## `maxPosts` (type: `integer`): How many recent posts and Reels to sample for the heatmap (1-500). More samples = a sharper pattern. ## `timezoneOffset` (type: `number`): The account's UTC offset in hours, e.g. -5 for US Eastern, 1 for Central Europe, 5.5 for India. Leave blank to auto-detect the time zone from the account's quietest posting window. ## Actor input object example ```json { "username": "natgeo", "maxPosts": 500 } ``` # Actor output Schema ## `report` (type: `string`): A single record: username, postsAnalyzed, timeRange, detected timezone (offset, label, confidence), mostActiveDay, mostActiveHour, quietHours, weekday/weekend split, bestTimesToPost (top slots by average engagement), topSlotsByPosts, hourHistogram (24), dayHistogram (7, Mon-first), avgEngagementByHour/Day, and full day-by-hour heatmaps for post counts (local + UTC) and average engagement. # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "username": "natgeo" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/instagram-active-hours-analyzer").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "username": "natgeo" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/instagram-active-hours-analyzer").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "username": "natgeo" }' | apify call seemuapps/instagram-active-hours-analyzer --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/instagram-active-hours-analyzer", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Instagram Active Hours & Best Time to Post Analyzer", "description": "Find the best time to post on Instagram for any public account — get a day-by-hour posting heatmap, peak engagement slots, and the account's likely time zone.", "version": "1.0", "x-build-id": "e2jtnZeMEEVPFyM2B" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~instagram-active-hours-analyzer/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-instagram-active-hours-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~instagram-active-hours-analyzer/runs": { "post": { "operationId": "runs-sync-seemuapps-instagram-active-hours-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~instagram-active-hours-analyzer/run-sync": { "post": { "operationId": "run-sync-seemuapps-instagram-active-hours-analyzer", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "username" ], "properties": { "username": { "title": "Username", "type": "string", "description": "Instagram username, with or without leading @. Profile URLs (https://www.instagram.com/username) are also accepted." }, "maxPosts": { "title": "Posts to Analyze", "minimum": 1, "maximum": 500, "type": "integer", "description": "How many recent posts and Reels to sample for the heatmap (1-500). More samples = a sharper pattern.", "default": 500 }, "timezoneOffset": { "title": "Time Zone Offset (optional)", "type": "number", "description": "The account's UTC offset in hours, e.g. -5 for US Eastern, 1 for Central Europe, 5.5 for India. Leave blank to auto-detect the time zone from the account's quietest posting window." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```